KLI

Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography

Metadata Downloads
Abstract
As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 +/- 8.4 mm and 4.1 +/- 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 +/- 15.4 mm and 12.1 +/- 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38-3.10 cm(2). A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.
Author(s)
하지연박태용김홍규신용빈고유선김동욱성유섭이지우함수정강성우정희령Kyoyeong Koo이정진김경원
Issued Date
2021
Type
Article
Keyword
AbdomenAbdominal Muscles - diagnostic imagingAccuracyAlgorithmsBody compositionBody Composition - physiologyComputational Biology - methodsComputed tomographyDatabasesFactualDeep LearningHumansImage ProcessingComputer-Assisted - methodsLumbar Vertebrae - diagnostic imagingMachine LearningMultidetector Computed Tomography - methodsNeural NetworksComputer SarcopeniaSarcopenia - diagnosisSegmentationSuccessTomographyX-Ray Computed - methodsVariationVertebrae
DOI
10.1038/s41598-021-00161-5
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7446
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_5ca3c1fc0916449b9470c19b4f93fb3e&amp;context=PC&amp;vid=ULSAN&amp;lang=ko_KR&amp;search_scope=default_scope&amp;adaptor=primo_central_multiple_fe&amp;tab=default_tab&amp;query=any,contains,Development%20of%20a%20fully%20automatic%20deep%20learning%20system%20for%20L3%20selection%20and%20body%20composition%20assessment%20on%20computed%20tomography&amp;offset=0&amp;pcAvailability=true
Publisher
SCIENTIFIC REPORTS
Location
영국
Language
영어
ISSN
2045-2322
Citation Volume
11
Citation Number
11
Citation Start Page
0
Citation End Page
0
Appears in Collections:
Medicine > Medicine
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.